Ultrasound tomography is an emerging modality for breast imaging. However, current ultrasonic tomography's imaging algorithms, hindered by the limited memory and processor speed of computers, are based on ray theory and assume a homogeneous background, which is inaccurate for complex heterogeneous regions and fails when the size of lesions is about the same size or smaller than the wavelength of ultrasound used. Therefore, wave theory must be used in ultrasonic imaging algorithms to properly handle the heterogeneous nature of breast tissue and the diffraction effects in order to accurately image small lesions. Moreover, by taking full advantage of Graphic Processing Units (GPUs) computational architecture, the intensive computation burden of waveform tomography can be greatly alleviated. The long term goal of this research is to develop novel and practical clinical breast imaging methods capable of regularly detecting lesions before they become metastatic. The objective of this Phase I study and the first step in the pursuit of that goal is to make waveform tomography practical in a clinical setting. Our central hypothesis is that the waveform tomography algorithms, implemented by utilizing the Delphinus Medical Technologies SoftVue acquisition geometry and computation architecture, will be able to produce high-accuracy and high-resolution breast images on clinically relevant time scales. The rationale for the proposed study is that a confluence of recent technical developments has enabled new approaches for dramatically improving the performance of ultrasound tomography, which would lead to major improvements in breast cancer detection and diagnosis. The expected outcome is a validation of the proposed technique that supports our long term goal of a practical, low-cost device for breast cancer detection and diagnosis. We will test our hypothesis and pursue our goals through the following specific aims. Aim 1: Adapt waveform ultrasound tomography algorithm to the SoftVue computational architecture. Aim 2: Assess the accuracy and resolution of the algorithm in Aim 1 using numerical breast models. Aim 3: Investigate the computational speed of the algorithm in Aim 1 for practical clinical applications using numerical breast models. PUBLIC HEALTH RELEVANCE: The proposed research explores novel waveform ultrasound tomography algorithms integrated with the computational architecture of our commercial blade-server reconstruction engine with embedded GPUs (SoftVue system) for rapid and optimal multi-parameter breast imaging. Successful outcome of the proposed technique will lead to major improvements in breast cancer imaging and support our long term goal of a practical, low-cost device for breast cancer detection and diagnosis.